Reducing churn rate for a social network service

ABSTRACT

Systems and methods for reducing a churn rate associated with subscribers of social network services are described. In some example embodiments, the systems and methods may access activity information associated with a former subscriber of a social network service, compare the accessed activity information to activity information associated with subscribers of the social network service, identify one or more differences between the activity information associated with the former subscriber of the social network service and the activity information associated with the subscribers of the social network service, and perform an action based on the identified one or more differences.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/892,117, filed on May 10, 2013, which application claims the benefitof priority to U.S. Provisional Application Ser. No. 61/806,345, filedon Mar. 28, 2013, which applications are hereby incorporated byreference in their entirety.

TECHNICAL FIELD

The present disclosure generally relates to data processing techniquesfor a subscription-based service. More specifically, the presentdisclosure relates to methods, systems and computer program products forreducing churn and/or improving retention of subscribers within a socialnetwork service.

BACKGROUND

Churn rate measures a number of individuals that leave a group or othercollection over a certain period of time, such as a number ofsubscribers that leave a subscription-based service. Churn, therefore,is similar to attrition, and may be the opposite of retention. Forexample, a subscriber-based service model may succeed when subscriberchurn is low (and retention is high), and may fail when subscriber churnis high (and retention is low), among other things.

Industries that rely on subscription-based service models, such as thecable television industry, the cell phone industry, web-based services,and so on, spend a considerable amount of time, money, and effortattempting to identify reasons why their subscribers churn, in order toprovide retention incentives to subscribers that keep them from endinguse of provided services. However, their efforts often lack insight orare driven by information received directly from subscribers or fromsimple metrics, which may lead to ineffective results and unsuccessfuldeterminations as to why subscribers are not being retained, among otherproblems.

DESCRIPTION OF THE DRAWINGS

Some embodiments of the technology are illustrated by way of example andnot limitation in the figures of the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating an example of a networkenvironment including a server operating a system for reducing a churnrate for a social network service, consistent with some embodiments.

FIG. 2 is a block diagram illustrating modules of a churn reductionsystem, consistent with some embodiments.

FIG. 3 is a schematic diagram illustrating an example comparison betweena churner and a collection of non-churners, consistent with someembodiments.

FIGS. 4A-4B are display diagrams illustrating the performance of anaction associated with reducing a churn rate for a social networkservice, consistent with some embodiments.

FIG. 5 is a flow diagram illustrating an example method for reducing achurn rate for a social network service, consistent with someembodiments.

FIG. 6 is a flow diagram illustrating an example method for performingan action based on a churn probability associated with a subscriber of asocial network service.

FIG. 7 is a block diagram of a machine, in the form of a computingdevice, within which a set of instructions for causing the machine toperform any one or more of the methodologies discussed herein, may beexecuted.

DETAILED DESCRIPTION

Overview

The present disclosure describes methods, systems, and computer programproducts, which individually provide functionality for reducing a churnrate for a social network service, such as by determining churnprobabilities for subscribers and/or other members of the social networkservice.

For example, the systems and methods may access activity informationassociated with a former subscriber of a social network service, comparethe accessed activity information to activity information associatedwith subscribers of the social network service, identify one or moredifferences between the activity information associated with the formersubscriber of the social network service and the activity informationassociated with the subscribers of the social network service, andperform an action based on the identified one or more differences, suchas an action that assists with retaining or renewing the formersubscriber to the social network service, among other things.

As another example, the systems and methods may receive and/or accessinput identifying a subscriber of a social network service, determine achurn or attrition probability for the subscriber of the social networkservice, and perform an action based on the determined churnprobability, such as an action to reduce the churn probability for thesubscriber, among other things.

Thus, by utilizing information retrieval and analysis techniques, asocial network service may accurately identify and reduce the churn rateassociated with subscribers of subscription-based services provided bythe social network service, enabling the social network service toincrease revenues associated with the provided services, provide betterand more targeted services to its subscribers and other members, and soon.

In the following description and for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the various aspects of different embodiments of thepresent invention. It will be evident, however, to one skilled in theart, that the present invention may be practiced without all of thespecific details.

Other advantages and aspects of the inventive subject matter will bereadily apparent from the description of the figures that follows.

Suitable System

FIG. 1 is a block diagram illustrating an example of a networkenvironment 100 including a server operating a system for reducing achurn rate for a social network service, consistent with someembodiments.

The network environment 100 includes a user device 140, such as a mobiledevice or other computing device, which accesses a social networkservice 110 over a network 130. The social network service 110 maysupport and/or provide a professional social network or any socialnetwork that includes members, where a member is connected to, friendswith, or otherwise affiliated with some of the other members of thenetwork 130. Thus, in some examples, the social network service 110includes a social graph that stores data identifying relationshipsbetween members of the social network. For example, social graph datamay indicate one member is a 1^(st) degree connection with anothermember when the members are directly connected, may indicate one memberis a 2^(nd) degree connection with another member when the members areindirectly connected via a third member (i.e., each of the members aredirectly connected to a third member but not directly connected to eachother), and so on.

In various example embodiments, one or more portions of the network 130may include an ad hoc network, an intranet, an extranet, a virtualprivate network (VPN), a local area network (LAN), a wireless LAN(WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitanarea network (MAN), a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a cellular telephone network, anyother type of network, or a combination of two or more such networks.The user device 140 may be any suitable computing device, such as asmart phone, a tablet, a laptop, gaming device, and/or any mobile deviceor computing device configured to view services and other informationprovided by the social network service 110 and receive selections fromusers of objects displayed by webpages, emails, and/or apps, among otherthings.

The social network service 110 may provide, via a supported socialnetwork, one or more free services 112 and/or one or moresubscriber-based services 114, such as enhanced or additional servicesthat are accessed by user devices 140 associated with paying and/orother subscribers of the services.

For example, the social network service 110 may provide the followingfree or non-subscription services 112 over the network 130 to a memberassociated with the user device 140:

Basic social functionality within a supported social network, such asthe ability to view limited profile information for non-connections;

Basic navigation and activities within the supported social network,such as access to a limited amount of analytics, the ability to send alimited number of messages to other members (e.g., non-connections);access to some or no analytics; and so on.

In contrast, the social network service 110 may, for example, providethe following subscription-based services 114 over the network 130 to amember associated with the user device 140:

Enhanced social functionality within the supported social network, suchas access to complete profiles for all members of the social network;

Enhanced navigation and other activities within the supported socialnetwork, such as access to analytics associated with members of thesocial network, analytics associated with messaging the members,activities of the members within the social network, activitiesassociated with actions performed within the social network; and so on.

As an example, a non-subscriber member of a professional social networkmay receive free services 112 such as services associated with viewingcomplete profiles for connections within the social network and sendingmessages to other connections, and so on. On the other hand, asubscriber member of the professional social network may receivesubscription services 114 not provided to the non-subscriber, such asservices associated with viewing the complete profiles of all members ofthe social network (e.g., connections and non-connections), servicesassociated with enhanced search and navigation capabilities within thesocial network, services associated with sending messages to anymembers, and so on.

A social network is a useful place in which to obtain various types ofinformation associated with services provided to members, such assubscription-based services 114, along with activities performed by themembers. Often, a social network or other similar site, such asLinkedIn, Facebook, Google+, Twitter, and so on, stores various types ofinformation or attributes associated with members of the site as memberprofile information. For example, a friend-based social networkingservice may store interest information for a member (e.g., informationabout things a member “likes”) in the member's profile, whereas aprofessional-based social networking site may store accomplishment orexperience information for a member (e.g., educational or workexperience information) in the member's profile, as well as activityinformation attributed to the members of the social networks.

Thus, the social network service 110 may contain, store, and/or haveaccess to (e.g., via a third party site) various types of memberactivity information, such as information stored within the subscriberdatabase 120. The subscriber database 120 may include informationassociated with members of the social network service 110, such asprofile information, social graph information, activity information, andso on.

For example, the subscriber database 120 may store informationassociated with the activities of members, such as the activitiesperformed by members of the social network within a supported socialnetwork. Example activity information stored for members of a socialnetwork may include:

Activity information associated with viewing profiles of other memberswithin the social network, such as information identifying a number ofprofile views, demographic information associated with the viewedprofiles, and so on;

Activity information associated with performing searches within thesocial network, such as information identifying a number of performedsearches, information identifying the search terms and/or results, andso on;

Activity information associated with messages sent within and/or out ofthe social network, such as information identifying a number of sentmessages, information identifying the recipients of the messages, and soon;

Activity information associated with performing analytics within thesocial network, such as a number of performed analyses, the type ofanalyses, and so on;

Activity information associated with generating and/or consuming contentwithin the social network, such as information identifying the type ofcontent generation (e.g., placing a job posting, posting a photo orlink, entering a status update or blog post) and/or consumption (viewinga job posting, viewing a photo or navigating to a posted link, viewing acompany or member profile); and so on.

The social network service 110, in some example embodiments, mayleverage such information in order to identify certain activities and/orother actions performed by members of a social network that are retainedas subscribers of subscription services 114 provided by the socialnetwork. Such subscribers may be considered non-churners, or subscribersthat maintain subscriptions to subscription-based services 114 betweenone time period to another (e.g. from a first month to a second month).On the other hand, subscribers that attrite or end subscriptions tosubscription-based services 114 between one time period to another maybe considered churners.

Thus, in order to leverage and/or utilize the information contained inthe subscriber database 120, such as the activity information associatedwith subscribers of subscription services 114, the social networkservice 110 may support and/or employ a churn reduction system 150 thatuses machine learning to reduce churn rates (e.g., attrition rates)associated with subscribers of the subscription services 114 provided bythe social network service 110, among other things.

Reducing Churn Rate for a Social Network Service

As described herein, in some example embodiments, the churn reductionsystem 150 utilizes various types of data or other information stored bya social network service 110 in order to reduce a churn rate associatedwith subscribers of the social network service 110, such as subscribersof one or more services provided by the social network service 110. FIG.2 is a block diagram illustrating modules of a churn reduction system,consistent with some embodiments.

As illustrated in FIG. 2, the churn reduction system 150 includes avariety of functional modules. One skilled in the art will appreciatethat the functional modules are implemented with a combination ofsoftware (e.g., executable instructions or computer code) and hardware(e.g., at least a memory and processor). Accordingly, as used herein, insome embodiments a module is a processor-implemented module andrepresents a computing device having a processor that is at leasttemporarily configured and/or programmed by executable instructionsstored in memory to perform one or more of the particular functions thatare described herein.

Referring to FIG. 2, the churn reduction system 150 includes aninformation module 210, a comparison module 220, an identificationmodule 230, an action module 240, and a probability module 250.

In some example embodiments, the information module 210 is configuredand/or programmed to access information associated with a formersubscriber of a social network service 110. For example, the informationmodule 210 may access activity information associated with a formersubscriber, or churner, of a subscription service 114 provided by thesocial network service 110. Example accessed activity information mayinclude activity information associated with a number of searchesperformed by the former subscriber during a subscription period, anumber of viewed profiles during the subscription period, a number ofmessages sent during the subscription period, and so on.

In some example embodiments, the comparison module 220 is configuredand/or programmed to compare the accessed activity information toactivity information associated with subscribers of the social networkservice 110. For example, the comparison module 220 may compare theactivity information of the former subscriber to other subscribers, ornon-churners, that retained subscriptions to the subscription service114 provided by the social network service 110.

In some cases, the comparison module 220 may compare a number ofsearches performed by the former subscriber within the social networkservice 110 to an average number of searches performed by current orretaining subscribers of the social network service 110, may compare anumber of profiles visited by the former subscriber within the socialnetwork service 110 to an average number of profiles visited by thecurrent or retaining subscribers of the social network service 110,among other activity types described herein.

In some example embodiments, the comparison module 220 may select agroup of cohorts to the former subscriber with which to perform thecomparison of activities. For example, the comparison module 220 mayconsider subscribers as cohorts to the former subscriber when they areassociated with a similar or same initial time period of beginning asubscription to a service (e.g., the same initial month as a subscriberto a service), when they are associated with a similar or sameprofessional group or organization (e.g., both members are job search orhuman resource professionals), when they are associated with a similaror same subscription to a service or services, and so on.

In some example embodiments, the identification module 230 is configuredand/or programmed to identify one or more differences between theactivity information associated with the former subscriber of the socialnetwork service 110 and the activity information associated with thesubscribers of the social network service 110. For example, theidentification module 230 may identify and/or determine activitiesperformed by non-churners of the social network service 110 that werenot performed, or not performed to a certain performance level, by theformer subscriber.

The identification module 230, therefore, may (1) identify an activitythat was performed by the subscribers of the social network service 110during an initial subscription time period, and was not performed by theformer subscriber of the social network service 110 during the initialsubscription time period, and/or (2) may identify an activity that wasperformed at a certain performance level by the subscribers of thesocial network service 110 during an initial subscription time periodand was not performed at the certain performance level by the formersubscriber of the social network service 110 during the initialsubscription time period, among other things.

For example, FIG. 3 is a schematic diagram 300 that illustrates anexample comparison of activities between a churner and a collection ofnon-churners, in order to identify differences of performed activities

The schematic diagram 300, or table or other data structure, includes anentry 310 for a churner (e.g., the former subscriber) and an entry 315for a group of non-churners (e.g., retained subscribers associated witha cohort that includes the former subscriber). The diagram 300 providescolumns of activities capable of being performed within the socialnetwork service 110, including a column 320 associated with an activityfor viewing profiles within the social network service 110, a column 322associated with sending messages within the social network service 110,a column 324 associated with an activity for performing searches withinthe social network service 110, and a column 326 associated with anaverage daily use of the social network service 110.

Based on the depicted comparison between activities of the churner andactivities of the group of non-churners, the identification module 230identifies differences in performed activities, such as differencesassociated with activities of sending messages, shown in column 322,within the social network service 110, and differences in usage levels,shown in column 326, of the social network service 110.

Of course, the diagram 300 may include other information not shown inFIG. 3, such as other activity types, performance levels of certainactivities, and other information described herein and associated withactivities performed by churners and non-churners within a socialnetwork service 110.

Referring back to FIG. 2, in some example embodiments, the action module240 is configured and/or programmed to perform an action based on theidentified one or more differences between the activity informationassociated with the former subscriber of the social network service 110and the activity information associated with the subscribers of thesocial network service 110. For example, the action module 240 mayperform an action associated with causing and/or prompting the formersubscriber to perform the identified activity, such as by providing arecommendation and/or suggestion to the former subscriber to perform theidentified activity, among other things.

The action module 240 may provide information or otherwise perform anaction to the former subscriber in a variety of ways. For example, theaction module 240 may present the former subscriber with arecommendation to perform an activity via a profile page associated withthe former subscriber within the social network service 110 and/or via adirect message, either within or outside a social network, from thesocial network service 110 to the former subscriber, such as from thesocial network service 110 to the user device 140, among othercommunication paths.

FIGS. 4A-4B are display diagrams illustrating the performance of anaction associated with reducing a churn rate for a social networkservice 110, consistent with some embodiments.

For example, FIG. 4A depicts a user interface 400 presented by the userdevice 140 that displays a profile page 410 of the social networkservice 110 that is associated with the former subscriber. The profilepage 410 displays various types of profile information 415 along with arecommendation 420 that may cause and/or prompt the former subscriber toperform an activity associated with non-churners, such as arecommendation reminding and/or providing information to the formersubscriber that all messages are free under a subscription model. Therecommendation 420 may present information as well as include actionableelements (e.g., links or buttons) in order to facilitate performance ofthe recommended activity and/or re-subscribing to the social networkservice 110 by the former subscriber, among other things.

As another example, FIG. 4B depicts a user interface 430 presented bythe user device 140 that displays a search page 440 of the socialnetwork service 110 currently viewed by the former subscriber. Thesearch page 440 displays search results 445 for a performed search 442of “Hadoop,” along with a suggestion 450 that may cause and/or promptthe former subscriber to perform an activity associated withnon-churners, such as a suggestion to filter the search resultspresented to the former subscriber. The suggestion 450 may presentinformation as well as include actionable elements (e.g., links orbuttons) in order to facilitate the former subscriber to perform therecommended activity and/or to re-subscribe to the social networkservice 110, among other things.

Of course, other information and/or user interfaces may be renderedand/or presented by the action module 240.

Referring back to FIG. 2, in some example embodiments, the probabilitymodule 250 is configured and/or programmed to determine a churnprobability for the subscriber of the social network service 110. Forexample, the probability module 250 may determine and/or calculate ametric or score to be assigned to a subscriber that identifies a churnor attrition probability for the subscriber at any time period during asubscription period associated with a service provided by the socialnetwork service 110, among other things.

The probability module 250 may determine a churn probability for a givensubscriber in a variety of ways. For example, the probability module 250may utilize a “churn meter” that is configured to receive, as input,information associated with a subscriber, and output a churn probabilitythat the subscriber will churn and end the subscription (or, on theother hand, output a retention probability that the subscriber willretain the subscription).

Example input that may be utilized by the churn meter when determining aprobability include profile information 415 for the subscriber (e.g.,job title, seniority, years at title, and so on), activity information(e.g., the frequency and/or intensity of performed activities), timeperiod information (e.g., when activities were performed), and so on.

The probability module 250, via the churn meter, may apply certainformulas and/or algorithms to input information in order to determine achurn probability for a certain subscriber at a certain point of timewithin a subscription period. For example, the probability module 250may assign an importance or weight to certain features or activitiesbased on various scoring formulas, such as the Fisher Score method, thePearson Coefficient method, and other analytical methods. Theprobability module 250 may then calculate probabilities based on thescores, such as by using Relative Time Derivative methods, among othertechniques.

For example, a churn curve may be defined as a logistic function,

${f(t)} = \frac{1}{1 + v^{- t}}$

The function may output a churn probability having a value between 0and 1. Using the function, the churn meter may determined and/orcalculate a churn probability for an individual subscriber by performinga simple vector product of each input variable (e.g. variables composedof raw and/or synthesized subscriber data from social networkingservices), with unique weights associated with each input variable. Thechurn probability, therefore, may be represented using the followingformula:

p(churn)=Σ₀ ^(i) x _(i) y _(i)

where x is a vector array of input variables (from 0 to i), and y is avector array of corresponding weights associated with each variable invector x.

For example, the probability module 250 may provide input for asubscriber that indicates the subscriber has (1) performed very fewsearches within the last 30 days of his/her subscription, (2) sent nodirect messages within the past 60 days, and (3) has a job title that isassociated with a senior level position. The churn meter may receivesuch inputs and output a high churn probability (e.g., P(churn)=50% orhigher).

Thus, in some example embodiments, the churn reduction system 150 maydetermine a churn probability for a subscriber of a service and thenperform actions associated with the determined churn probability, amongother things. The churn reduction system 150 may intake varioussubscriber data, such as demographic and behavioral data generated by asubscriber's usage of social networking services. Example date mayinclude data uniquely available on social networking services, such asdata associated with forming connections with other members, sharing andliking of content, and/or other social gestures that may be instrumentedas raw variables and/or synthesized with temporal or other dimensionalcriteria to assess a subscriber's probability to churn.

As described herein, in some example embodiments, the churn reductionsystem 150 performs various methods and/or techniques in order to reduceand/or prevent churn rates associated with services provided by thesocial network service 110. FIG. 5 is a flow diagram illustrating anexample method 500 for reducing a churn rate for a social networkservice 110, consistent with some embodiments. The method 500 may beperformed by the churn reduction system 150 and, accordingly, isdescribed herein merely by way of reference thereto. It will beappreciated that the method 500 may be performed on any suitablehardware.

In operation 510, the churn reduction system 150 accesses activityinformation associated with a former subscriber of a social networkservice 110. For example, the information module 210 accesses activityinformation associated with a former subscriber of subscription services114 provided by the social network service 110.

In operation 520, the churn reduction system 150 compares the accessedactivity information to activity information associated with subscribersof the social network service 110. For example, the comparison module220 compares the accessed activity information to activity informationassociated with subscribers of the social network service 110 that aremembers of a same professional group and/or cohort within the socialnetwork service 110.

As described herein, the comparison module 220 may compare variousactivities performed (or not performed) by a former subscriber and othersubscribers of the subscription services 114 provided by the socialnetwork service 110. Example comparisons include a comparison between anumber of searches performed by the former subscriber within the socialnetwork service 110 to an average number of searches performed bysubscribers of the social network service 110, a comparison between anumber of profile views performed by the former subscriber within thesocial network 110 service to an average number of average profile viewsperformed by subscribers of the social network service 110, a comparisonbetween a number of messages sent by the former subscriber within thesocial network service 110 to an average number of messages sent bysubscribers of the social network service 110, and so on.

In operation 530, the churn reduction system 150 identifies one or moredifferences between the activity information associated with the formersubscriber of the social network service 110 and the activityinformation associated with the subscribers of the social networkservice 110. For example, the identification module 230 may identify anactivity that was performed by the subscribers of the social networkservice 110 and was not performed by the former subscriber of the socialnetwork service 110, and/or an activity that was performed at a certainperformance level by the subscribers of the social network service 110and was not performed by the former subscriber of the social networkservice 110 at the certain performance level, among other things.

As described herein, the identification module 230 may identify variousdifferences between activities performed by churners and non-churners.For example, the identification module 230 may identify an activity thatwas performed by the subscribers of the social network service during aninitial subscription time period (or, other time periods) and was notperformed by the former subscriber of the social network service duringthe initial subscription time period (or, other time periods), mayidentify an activity that was performed at a certain performance levelby the subscribers of the social network service during an initialsubscription time period and was not performed at the certainperformance level by the former subscriber of the social network serviceduring the initial subscription time period, and so on.

Thus, in some example embodiments, the identification module 230identifies differences between the frequency and/or intensity ofperformance of an activity between former and current subscribers (e.g.,churners and non-churners), among other things.

In operation 540, the churn reduction system 150 performs an actionbased on the identified one or more differences between the activityinformation associated with the former subscriber of the social networkservice 110 and the activity information associated with the subscribersof the social network service 110. For example the action module 240performs an action associated with causing and/or prompting the formersubscriber to perform the activity.

As described herein, the action module 240 may perform a variety ofactions, such as provide recommendations or suggestions, associated withreducing a churn rate for a subscription service 114 provided by thesocial network service 110. For example, the action module 240 maypresent (or, cause to be presented) the former subscriber with arecommendation to perform an activity via a profile page associated withthe former subscriber within the social network service 110, may present(or, cause to be presented) the former subscriber with a recommendationto perform an activity via a direct message from the social networkservice 110 to the former subscriber, and so on.

Thus, in some example embodiments, the churn reduction system 150 mayreceive information identifying a subscriber of a social network service110 as a churner, determine an activity within the social networkservice 110 that was sub-standardly performed by the subscriber, andperform an action associated with causing and/or prompting thesubscriber to perform the determined activity. For example, the churnreduction system 150 may determine that the subscriber identified as achurner performed the activity at a performance level below aperformance level for the activity that was performed by othersubscribers of the social network service identified as non-churners,and perform an action associated with that activity, among other things.

As described herein, the churn reduction system 150, in some exampleembodiments, may proactively identify potential churners by determiningchurn probabilities for some or all subscribers of the social networkservice 110. The churn reduction system 150 may perform actions based onthe assigned probabilities, in order to reduce and/or prevent certainsubscribers from churning or otherwise ending subscriptions to services,among other things.

FIG. 6 is a flow diagram illustrating an example method 600 forperforming an action based on a churn probability associated with asubscriber of social network service 110. The method 600 may beperformed by the churn reduction system 150 and, accordingly, isdescribed herein merely by way of reference thereto. It will beappreciated that the method 600 may be performed on any suitablehardware.

In operation 610, the churn reduction system 150 receives inputassociated with a subscriber of a social network service 110. Forexample, the information module 210 accesses and/or receives informationassociated with a subscriber of services 114 provided by the socialnetwork service 110, such as the information described herein.

In operation 620, the churn reduction system 150 determines a churnprobability for the subscriber of the social network service 110. Forexample, the probability module 250 utilizes a churn meter to determinea probability to be assigned to the subscriber based on the informationassociated with the subscriber.

As described herein, the churn meter may assign weights and/or othervalues to various activities performed by the subscriber, and determinethe churn probability based on the weighted activities. For example, thechurn meter may weight searching for information within the socialnetwork service 110 and viewing profiles associated with members of thesocial network service 110 higher than sending messages to members ofthe social network service 110, and determine a probability based on theassigned weights.

In operation 630, the churn reduction system 150 performs an actionbased on the determined churn probability. For example, the actionmodule 240 performs an action associated with causing and/or promptingthe former subscriber to perform the activity. The action module 240 mayperform a variety of actions, such as provide recommendations orsuggestions associated with reducing a churn rate for a subscriptionservice 114 provided by the social network service 110 and/or actionsassociated with causing the subscriber to perform an activity within thesocial network service 110.

For example, the action module 240 may present (or, cause to bepresented) the former subscriber with a recommendation to perform anactivity via a profile page associated with the former subscriber withinthe social network service 110, may present (or, cause to be presented)the former subscriber with a recommendation to perform an activity via adirect message from the social network service 110 to the formersubscriber, and so on.

Thus, in some example embodiments, the churn reduction system 150determines a retention probability to assign to a subscriber of aservice that is based on activities provided by the service that areperformed by the member, and performs an action associated with thedetermined retention probability. The retention probability may be basedon a variety of factors, such as a performance level achieved by thesubscriber for each of the activities provided by the service that areperformed by the member, attributes associated with the subscriber, andso on.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedmodules, engines, objects or devices that operate to perform one or moreoperations or functions. The modules, engines, objects and devicesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules, engines, objects and/or devices.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or more processors orprocessor-implemented modules. The performance of certain operations maybe distributed among the one or more processors, not only residingwithin a single machine or computer, but deployed across a number ofmachines or computers. In some example embodiments, the processor orprocessors may be located in a single location (e.g., within a homeenvironment, an office environment or at a server farm), while in otherembodiments the processors may be distributed across a number oflocations.

FIG. 7 is a block diagram of a machine 1500 in the form of a computersystem 1500 or computing device within which a set of instructions 1524,for causing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed. In alternative embodiments, themachine operates as a standalone device or may be connected (e.g.,networked) to other machines. In a networked deployment, the machine mayoperate in the capacity of a server or a client machine in aclient-server network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. In some exampleembodiments, the machine will be a desktop computer or server computer;however, in alternative embodiments, the machine may be a tabletcomputer, a mobile phone, a personal digital assistant, a personal audioor video player, a global positioning device, a set-top box, a webappliance, or any machine capable of executing instructions 1524(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” shall also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructions1524 to perform any one or more of the methodologies discussed herein.

The example computer system 1500 includes a processor 1502 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 1504 and a static memory 1506, which communicatewith each other via a bus 1508. The computer system 1500 may furtherinclude a graphics display unit 1510, an alphanumeric input device 1512(e.g., a keyboard), and a user interface (UI) navigation device (e.g., amouse). In one embodiment, the graphics display 1510, alphanumeric inputdevice 1512 and cursor control device 1514 are a touch screen display.The computer system 1500 may additionally include a storage unit device1516 (e.g., drive unit), a signal generation device 1518 (e.g., aspeaker), a network interface device 1520, and one or more sensors, suchas a global positioning system sensor, compass, accelerometer, or othersensor.

The storage unit 1516 includes a machine-readable medium 1522 on whichis stored one or more sets of instructions 1524 and data structures(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The software 1523 may alsoreside, completely or at least partially, within the main memory 1504and/or within the processor 1502 during execution thereof by thecomputer system 1500, the main memory 1504 and the processor 1502 alsoconstituting machine-readable media.

While the machine-readable medium 1522 is illustrated in an exampleembodiment to be a single medium, the term “machine-readable medium” mayinclude a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more instructions. The term “machine-readable medium” shallalso be taken to include any tangible medium that is capable of storing,encoding or carrying instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresent invention, or that is capable of storing, encoding or carryingdata structures utilized by or associated with such instructions. Theterm “machine-readable medium” shall accordingly be taken to include,but not be limited to, solid-state memories, and optical and magneticmedia. Specific examples of machine-readable media include non-volatilememory, including by way of example semiconductor memory devices, e.g.,EPROM, EEPROM, and flash memory devices; magnetic disks such as internalhard disks and removable disks; magneto-optical disks; and CD-ROM andDVD-ROM disks.

The software may further be transmitted or received over acommunications network 1526 using a transmission medium via the networkinterface device 1520 utilizing any one of a number of well-knowntransfer protocols (e.g., HTTP). Examples of communication networks 1526include a local area network (“LAN”), a wide area network (“WAN”), theInternet, mobile telephone networks, Plain Old Telephone (POTS)networks, and wireless data networks (e.g., Wi-Fi® and WiMax® networks).The term “transmission medium” shall be taken to include any intangiblemedium that is capable of storing, encoding or carrying instructions1524 for execution by the machine, and includes digital or analogcommunications signals or other intangible medium to facilitatecommunication of such software.

Although an embodiment has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof, show by way of illustration and not of limitation, specificembodiments in which the subject matter may be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments may be utilized and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. This Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

1. (canceled)
 2. A system, comprising: a processor for executinginstructions stored in a memory device; at least one memory devicestoring instructions, said instructions comprising: comparing firstactivity information of at least one current subscriber of a softwareservice to second activity information of a particular currentsubscriber of the software service, the first activity informationrepresenting at least one type of activity of the software serviceperformed by the at least one current subscriber, the second activityinformation representing at least one type of activity of the softwareservice performed by the particular current subscriber; determining aprobability that the particular current subscriber will terminatesoftware service subscription, the probability based at least on adifference of a first performance level of the at least one type ofactivity in the first activity information and a second performancelevel of the at least one type of activity in the second activityinformation; and causing display of a prompt to the particular currentsubscriber, the prompt indicating a particular type of activity to beperformed by the particular current subscriber to reduce the probabilitythat the particular current subscriber will terminate software servicesubscription.
 3. The system of claim 2, wherein comparing first activityinformation of at least one former subscriber of a software service tosecond activity information of a current subscriber of the softwareservice includes: identifying a first type of activity represented inboth the first and the second activity information.
 4. The system ofclaim 3, wherein identifying a first type of activity represented inboth first and the second activity information includes: identifying atleast one respective instance of the first type of activity in both thefirst and the second activity information, each respective instance ofthe first type of activity performed during an initial subscription timeperiod.
 5. The system of claim 3, wherein determining a probability thatthe current subscriber will terminate software service subscriptionfurther comprises: determining the probability based in part on a typeof job title present in profile data of the particular currentsubscriber.
 6. The system of claim 5, wherein the type of job titlerepresent a senior level job title.
 7. The system of claim 3, whereinthe first type of activity comprises an average number of searches inthe software searches.
 8. The system of claim 3, wherein the first typeof activity comprises an average number of software service profileviews.
 9. The system of claim 3, wherein the first type of activitycomprises an average number of messages sent within the softwareservice.
 10. A computer-implemented method comprising: comparing firstactivity information of at least one current subscriber of a softwareservice to second activity information of a particular currentsubscriber of the software service, the first activity informationrepresenting at least one type of activity of the software serviceperformed by the at least one current subscriber, the second activityinformation representing at least one type of activity of the softwareservice performed by the particular current subscriber; determining, viaat least one hardware processor, a probability that the particularcurrent subscriber will terminate software service subscription, theprobability based at least on a difference of a first performance levelof the at least one type of activity in the first activity informationand a second performance level of the at least one type of activity inthe second activity information; and causing display of a prompt to theparticular current subscriber, the prompt indicating a particular typeof activity to be performed by the particular current subscriber toreduce the probability that the particular current subscriber willterminate software service subscription.
 11. The computer-implementedmethod of claim 10, wherein comparing first activity information of atleast one former subscriber of a software service to second activityinformation of a current subscriber of the software service includes:identifying a first type of activity represented in both the first andthe second activity information.
 12. The computer-implemented method ofclaim 11, wherein identifying a first type of activity represented inboth the first and the second activity information includes: identifyingat least one respective instance of the first type of activity in boththe first and the second activity information; each respective instanceof the first type of activity performed during an initial subscriptiontime period.
 13. The computer-implemented method of claim 11, whereindetermining a probability that the current subscriber will terminatesoftware service subscription further comprises: determining theprobability based in part on a type of job title present in profile dataof the particular current subscriber.
 14. The computer-implementedmethod of claim 13, wherein the type of job title represent a seniorlevel job title.
 15. The computer-implemented method of claim 11,wherein the first type of activity comprises an average number ofsearches in the software searches.
 16. The computer-implemented methodof claim 11, wherein the first type of activity comprises an averagenumber of software service profile views.
 17. The computer-implementedmethod of claim 11, wherein the first type of activity comprises anaverage number of messages sent within the software service.
 18. Anon-transitory computer-readable storage medium whose contents, whenexecuted by a computing system, cause the computing system to performoperations, comprising: comparing first activity information of at leastone current subscriber of a software service to second activityinformation of a particular current subscriber of the software service,the first activity information representing at least one type ofactivity of the software service performed by the at least one currentsubscriber, the second activity information representing at least onetype of activity of the software service performed by the particularcurrent subscriber; determining a probability that the particularcurrent subscriber will terminate software service subscription, theprobability based at least on a difference of a first performance levelof the at least one type of activity in the first activity informationand a second performance level of the at least one type of activity inthe second activity information; and causing display of a prompt to theparticular current subscriber, the prompt indicating a particular typeof activity to be performed by the particular current subscriber toreduce the probability that the particular current subscriber willterminate software service subscription.
 19. The non-transitorycomputer-readable storage medium of claim 18, wherein comparing firstactivity information of at least one former subscriber of a softwareservice to second activity information of a current subscriber of thesoftware service includes: identifying a first type of activityrepresented in both the first and the second activity information. 20.The non-transitory computer-readable storage medium of claim 19, whereinidentifying a first type of activity represented in both the first andthe second activity information includes: identifying at least onerespective instance of the first type of activity in both the first andthe second activity information, each respective instance of the firsttype of activity performed during an initial subscription time period.21. The non-transitory computer-readable storage medium of claim 19,wherein determining a probability that the current subscriber willterminate software service subscription further comprises: determiningthe probability based in part on a type of job title present in profiledata of the particular current subscriber.